September 20, 2012
Jose M. Carmena, Ph.D.
Associate Professor, Helen Wills Neuroscience Institute
Dept. of Electrical Engineering & Computer Sciences
University of California, Berkeley
Brain-machine interface (BMI) systems have enormous potential to improve
the quality of life for large numbers of neurological patients. BMIs
also provide a framework for studying cortical dynamics and the neural
correlates of learning neuroprosthetic skills, i.e. accurate,
readily-recalled control of disembodied actuators irrespective of
natural physical movements. In this talk I will present recent results
showing that: 1) the brain can consolidate neuroprosthetic motor skill
in a way that resembles that of natural motor learning; 2) proficient
neuroprosthetic control reversibly reshapes cortical networks through
local effects; 3) learned neuroprosthetic actions are intentional and
goal-directed, rather than habitual; and 4) corticostriatal plasticity
is necessary for neuroprosthetic skill learning. This will be followed
by discussion on BMI systems design with the goal of developing
neuroprosthetic devices for the impaired.
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